1.Preprocessing

1.1 Remove NA Values

data <- read.csv("https://github.com/lypf2018/HousePricesKaggle/raw/master/dataset/train.csv")
# View(data)
probNA <- function(x){
  sum(is.na(x))/nrow(data)
}
probNAcol <- apply(data, 2, probNA)
as.data.frame(probNAcol[probNAcol>0.3])

Obviously, there are many NA values(percentage of NA values bigger than 30%) in above 5 columns. We decided to remove these columns then remove all other NA values.

data[c("Alley","FireplaceQu","PoolQC","Fence","MiscFeature")] <- NULL
data["Id"] <- NULL
data <- na.omit(data)

1.2 Remove Non-numeric Freatures

Since there are enough features(80 features) in our data, we decide to directly remove all non-numeric features. The numeric features also include some categorical data, which use numbers to present different categories.

dataNum <- as.data.frame(data[,apply(data, 2, function(x) !any(is.na(as.numeric(x))))])
## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

## Warning in FUN(newX[, i], ...): NAs introduced by coercion

2. SalesPrice Analysis

summary(dataNum$SalePrice)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   35311  132500  165750  187033  221000  755000
hist(dataNum$SalePrice, ylim = c(0, 8*10^-6), probability = TRUE)
lines(density(dataNum$SalePrice))

finding: 1. From summary of SalesPrice: Minimal house price is largger than 0, so it would not destroy our model. 2. From histogram of SalesPrice: the distribution 1) Deviate from the normal distribution. 2) Have appreciable positive skewness. 3) Show peakedness.

It seems that there are few extremly rich peolple bought very expensive houses, which makes the distribution has a long tail on the right, that is, positive skewness.

2. Correlation Analysis

2.1 Correlation Plot

require(MASS)
## Loading required package: MASS
require(ISLR)
## Loading required package: ISLR
require(corrplot)
## Loading required package: corrplot
## corrplot 0.84 loaded
M <- cor(dataNum)
corMat <- as.data.frame(corrplot(M,method = "number"))

corrplot(cor(dataNum[row.names(corMat)[abs(corMat$SalePrice) > 0.50]]), method = "number")

finding: From the correlation plot we can see the 11 features with the strongest effect(correlation > 0.5) on SalePrice:

1.OverallQual: Rates the overall material and finish of the house. (1 very poor - 10 very excellent). 2.YearBuilt: Original construction date. 3.YearRemodAdd: Remodel date (same as construction date if no remodeling or additions). 4.TotalBsmtSF: Total square feet of basement area. 5.X1stFlrSF: First Floor square feet. 6.GrLivArea: Above grade (ground) living area square feet. 7.FullBath: Full bathrooms above grade. 8.TotRmsAbvGrd: Total rooms above grade (does not include bathrooms). 9.GarageYrBlt: Year garage was built. 10.GarageCars: Size of garage in car capacity. 11.GarageArea: Size of garage in square feet.

All of them are positive correlations.

We can see there are also some strong relations between these features. For example, correlation between TotalBsmtSF and X1stFlrSF is 0.91, correlation between GrLivArea and TotRmsAbvGrd is 0.83. That make sense since a big basement area is always together with a big area first floor, and the size of the living area will most likely be a constraint on the number of rooms above ground.

2.2 Scatter Plots

We can print a matrix of scatter plots to see what the relationships between features look like.

require(GGally)
## Loading required package: GGally
## Loading required package: ggplot2
corr.idx <- row.names(corMat)[abs(corMat$SalePrice) > 0.5]
lm.plt <- function(data, mapping, ...){
   plt <- ggplot(data = data, mapping = mapping) + 
    geom_point(shape = 20, alpha = 0.7, color = 'darkseagreen') +
    geom_smooth(method=loess, fill="red", color="red") +
    geom_smooth(method=lm, fill="blue", color="blue") +
    theme_minimal()
  return(plt)
}
ggpairs(dataNum, corr.idx[c(12,1:6)], lower = list(continuous = lm.plt))
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 6
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 6
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 6
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 6
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 6
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 6
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 6
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 6
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 6
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 6
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 0

ggpairs(dataNum, corr.idx[c(12,7:11)], lower = list(continuous = lm.plt))
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at -0.015
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.015
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 3.5817e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0602
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at -0.015
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.015
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 3.5817e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other
## near singularities as well. 4.0602
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at -0.015
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.015
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 3.5817e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0602
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at -0.015
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.015
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 3.5817e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other
## near singularities as well. 4.0602
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at -0.015
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.015
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 3.5817e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0602
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at -0.015
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.015
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 3.5817e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other
## near singularities as well. 4.0602
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at -0.015
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.015
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 3.5817e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0602
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at -0.015
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.015
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 3.5817e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other
## near singularities as well. 4.0602
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.985
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.015
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 4.3777e-31
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 0.985
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.015
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 4.3777e-31
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other
## near singularities as well. 1

2.3 Single Feature Analysis

2.3.1 categorical feature

lm.fit = lm(SalePrice~OverallQual, data = dataNum)
summary(lm.fit) 
## 
## Call:
## lm(formula = SalePrice ~ OverallQual, data = dataNum)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -208643  -31369   -1227   21325  386357 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -115355       7137  -16.16   <2e-16 ***
## OverallQual    48400       1116   43.37   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 50420 on 1092 degrees of freedom
## Multiple R-squared:  0.6327, Adjusted R-squared:  0.6324 
## F-statistic:  1881 on 1 and 1092 DF,  p-value: < 2.2e-16
boxplot(dataNum$SalePrice~dataNum$OverallQual)
abline(lm.fit,lwd = 2, col="red")

finding: 小和大的房子少，中间大小的房子多。房子面积越大，价格越分散，价格差距非常大（6e+05的差距）有很贵的也有很便宜的，而中间大小的房子价格比较集中。

lm.fit2 = lm(SalePrice~GarageCars, data = dataNum)
summary(lm.fit2) 
## 
## Call:
## lm(formula = SalePrice ~ GarageCars, data = dataNum)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -238662  -37060   -4546   26571  475684 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    32276       5769   5.595 2.79e-08 ***
## GarageCars     82347       2897  28.424  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 63080 on 1092 degrees of freedom
## Multiple R-squared:  0.4252, Adjusted R-squared:  0.4247 
## F-statistic: 807.9 on 1 and 1092 DF,  p-value: < 2.2e-16
boxplot(dataNum$SalePrice~dataNum$GarageCars)
abline(lm.fit2,lwd = 2, col="red")

finding: 车库四个车位 没有必要 所以买不起好价钱

2.3.2 numeric feature

lm.fit3 = lm(SalePrice~GrLivArea, data = dataNum)
summary(lm.fit3) 
## 
## Call:
## lm(formula = SalePrice ~ GrLivArea, data = dataNum)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -486328  -29378   -2472   21283  331917 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 15366.836   5485.443   2.801  0.00518 ** 
## GrLivArea     111.833      3.381  33.080  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 58800 on 1092 degrees of freedom
## Multiple R-squared:  0.5005, Adjusted R-squared:  0.5001 
## F-statistic:  1094 on 1 and 1092 DF,  p-value: < 2.2e-16
plot(dataNum$GrLivArea, dataNum$SalePrice)
abline(lm.fit3,lwd = 2, col="red")

finding： 合理完美的线性，点均匀的分布在线的两边

lm.fit4 = lm(SalePrice~TotalBsmtSF, data = dataNum)
summary(lm.fit4)
## 
## Call:
## lm(formula = SalePrice ~ TotalBsmtSF, data = dataNum)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -646027  -40185  -15144   34717  401874 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 51192.531   5594.361   9.151   <2e-16 ***
## TotalBsmtSF   123.541      4.759  25.959   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 65430 on 1092 degrees of freedom
## Multiple R-squared:  0.3816, Adjusted R-squared:  0.381 
## F-statistic: 673.9 on 1 and 1092 DF,  p-value: < 2.2e-16
plot(dataNum$TotalBsmtSF, dataNum$SalePrice)
abline(lm.fit4,lwd = 2, col="red")

2.4 Multiple Feature Analysis

lm.fit5 = lm(SalePrice~OverallQual+GarageCars+GrLivArea, data = dataNum)
summary(lm.fit5)
## 
## Call:
## lm(formula = SalePrice ~ OverallQual + GarageCars + GrLivArea, 
##     data = dataNum)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -342376  -23732   -1488   20692  291962 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.202e+05  6.046e+03  -19.88   <2e-16 ***
## OverallQual  2.921e+04  1.328e+03   22.00   <2e-16 ***
## GarageCars   2.608e+04  2.515e+03   10.37   <2e-16 ***
## GrLivArea    4.933e+01  3.162e+00   15.60   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42690 on 1090 degrees of freedom
## Multiple R-squared:  0.7373, Adjusted R-squared:  0.7365 
## F-statistic:  1020 on 3 and 1090 DF,  p-value: < 2.2e-16
lm.fit6 = lm(SalePrice~OverallQual+GarageCars+GrLivArea+TotalBsmtSF+BsmtUnfSF+MSSubClass, data = dataNum)
summary(lm.fit6)
## 
## Call:
## lm(formula = SalePrice ~ OverallQual + GarageCars + GrLivArea + 
##     TotalBsmtSF + BsmtUnfSF + MSSubClass, data = dataNum)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -482060  -18994   -1628   17414  275331 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.116e+05  5.821e+03 -19.173  < 2e-16 ***
## OverallQual  2.812e+04  1.287e+03  21.857  < 2e-16 ***
## GarageCars   2.334e+04  2.382e+03   9.799  < 2e-16 ***
## GrLivArea    4.697e+01  3.027e+00  15.515  < 2e-16 ***
## TotalBsmtSF  3.016e+01  3.921e+00   7.693 3.21e-14 ***
## BsmtUnfSF   -2.463e+01  2.919e+00  -8.437  < 2e-16 ***
## MSSubClass  -2.002e+02  3.082e+01  -6.498 1.24e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39530 on 1087 degrees of freedom
## Multiple R-squared:  0.7753, Adjusted R-squared:  0.7741 
## F-statistic: 625.1 on 6 and 1087 DF,  p-value: < 2.2e-16